222 research outputs found
Video Action Recognition with Attentive Semantic Units
Visual-Language Models (VLMs) have significantly advanced action video
recognition. Supervised by the semantics of action labels, recent works adapt
the visual branch of VLMs to learn video representations. Despite the
effectiveness proved by these works, we believe that the potential of VLMs has
yet to be fully harnessed. In light of this, we exploit the semantic units (SU)
hiding behind the action labels and leverage their correlations with
fine-grained items in frames for more accurate action recognition. SUs are
entities extracted from the language descriptions of the entire action set,
including body parts, objects, scenes, and motions. To further enhance the
alignments between visual contents and the SUs, we introduce a multi-region
module (MRA) to the visual branch of the VLM. The MRA allows the perception of
region-aware visual features beyond the original global feature. Our method
adaptively attends to and selects relevant SUs with visual features of frames.
With a cross-modal decoder, the selected SUs serve to decode spatiotemporal
video representations. In summary, the SUs as the medium can boost
discriminative ability and transferability. Specifically, in fully-supervised
learning, our method achieved 87.8% top-1 accuracy on Kinetics-400. In K=2
few-shot experiments, our method surpassed the previous state-of-the-art by
+7.1% and +15.0% on HMDB-51 and UCF-101, respectively.Comment: Accepted at ICCV 202
Structure basis for the unique specificity of medaka enteropeptidase light chain
Thermal stresses concern not renewed type of stresses, that is once having liberated, they cannot accumulate more. The estimation of purely thermoelastic contribution to a lithosphere stress state gives the additional information, allowing to predict the danger connected with such natural factors, as seismic and volcanic activity. Some theoretical thermoelastic problems for the geological environment of a difficult outline with non-uniform thermophysical characteristics are considered. The decision is received on the basis of a numerical finite elements method. Influence of the model fixation, the geometrical factor and boundary conditions on distribution of thermal stresses and dislocation is investigated. Computing experiments have shown, that the size of the maximum thermal stresses reaches 500 bar. The maximum values of vertical dislocation are reached by 90 m, and horizontal — 50 m. Neutral plane position are precisely defined. Термоупругие напряжения относятся к невозобновляемому типу напряжений, то есть, однажды высвободившись, напряжения не могут накапливаться вновь. Расчет термоупругого вклада в напряженное состояние литосферы дает дополнительную информацию, позволяющую оценить опасность, связанную с такими природными явлениями, как сейсмичность и вулканическая активность. Рассмотрено несколько теоретических моделей для геологической среды сложного очертания с неоднородными теплофизическими характеристиками. Решение получено на основе численного метода конечных элементов. Исследовано влияние «закрепления» модели, геометрического фактора, неоднородных граничных условий на распределение термоупругих напряжений и перемещений. Вычислительные эксперименты показали, что величина максимальных термоупругих напряжений достигает 500 б. Максимальные величины вертикальных перемещений не превышают 90 м, горизонтальных — 50 м. Положение нейтральной плоскости определяется точно. На основі методу скінченних елементів отримано детальний розподіл термопружних напружень і переміщень для неоднорідного геологічного середовища. Досліджено взаємний вплив геометрії середовища й неоднорідних граничних умов на розподіл термопружних напружень та переміщень
Long-distance propagation of high-velocity antiferromagnetic spin waves
We report on coherent propagation of antiferromagnetic (AFM) spin waves over
a long distance (10 m) at room temperature in a canted AFM
-FeO with the Dzyaloshinskii-Moriya interaction (DMI).
Unprecedented high group velocities (up to 22.5 km/s) are characterized by
microwave transmission using all-electrical spin wave spectroscopy. We derive
analytically AFM spin-wave dispersion in the presence of the DMI which accounts
for our experimental results. The AFM spin waves excited by nanometric coplanar
waveguides with large wavevectors enter the exchange regime and follow a
quasi-linear dispersion relation. Fitting of experimental data with our
theoretical model yields an AFM exchange stiffness length of 1.7 angstrom. Our
results provide key insights on AFM spin dynamics and demonstrate high-speed
functionality for AFM magnonics
Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy
Scanning electron microscopy (SEM) is indispensable in diverse applications
ranging from microelectronics to food processing because it provides large
depth-of-field images with a resolution beyond the optical diffraction limit.
However, the technology requires coating conductive films on insulator samples
and a vacuum environment. We use deep learning to obtain the mapping
relationship between optical super-resolution (OSR) images and SEM domain
images, which enables the transformation of OSR images into SEM-like large
depth-of-field images. Our custom-built scanning superlens microscopy (SSUM)
system, which requires neither coating samples by conductive films nor a vacuum
environment, is used to acquire the OSR images with features down to ~80 nm.
The peak signal-to-noise ratio (PSNR) and structural similarity index measure
values indicate that the deep learning method performs excellently in
image-to-image translation, with a PSNR improvement of about 0.74 dB over the
optical super-resolution images. The proposed method provides a high level of
detail in the reconstructed results, indicating that it has broad applicability
to chip-level defect detection, biological sample analysis, forensics, and
various other fields.Comment: 13 pages,7 figure
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